Comparing Dataset A and Dataset B

Dataset statistics

	Dataset A	Dataset B
Number of variables	12	12
Number of observations	446	446
Missing cells	428	431
Missing cells (%)	8.0%	8.1%
Duplicate rows	0	0
Duplicate rows (%)	0.0%	0.0%
Total size in memory	45.3 KiB	45.3 KiB
Average record size in memory	104.0 B	104.0 B

Variable types

	Dataset A	Dataset B
Numeric	5	5
Categorical	4	4
Text	3	3

Alerts

Dataset A	Dataset B
`Age` has 84 (18.8%) missing values	`Age` has 87 (19.5%) missing values	Missing
`Cabin` has 343 (76.9%) missing values	`Cabin` has 344 (77.1%) missing values	Missing
`PassengerId` has unique values	`PassengerId` has unique values	Unique
`Name` has unique values	`Name` has unique values	Unique
`SibSp` has 306 (68.6%) zeros	`SibSp` has 293 (65.7%) zeros	Zeros
`Parch` has 337 (75.6%) zeros	`Parch` has 339 (76.0%) zeros	Zeros
`Fare` has 9 (2.0%) zeros	`Fare` has 7 (1.6%) zeros	Zeros

Reproduction

	Dataset A	Dataset B
Analysis started	2024-03-18 18:33:39.300534	2024-03-18 18:33:43.302154
Analysis finished	2024-03-18 18:33:43.301050	2024-03-18 18:33:46.300350
Duration	4 seconds	3 seconds
Software version	ydata-profiling v0.0.dev0	ydata-profiling v0.0.dev0
Download configuration	config.json	config.json

PassengerId
Real number (ℝ)

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	429.80717	435.92152

	Dataset A	Dataset B
Minimum	1	2
Maximum	891	887
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	1	2
5-th percentile	42.5	47.25
Q1	208.25	217.5
median	413	432.5
Q3	648.75	667.25
95-th percentile	841.75	830
Maximum	891	887
Range	890	885
Interquartile range (IQR)	440.5	449.75

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	256.13708	256.90053
Coefficient of variation (CV)	0.59593487	0.58932748
Kurtosis	-1.1850064	-1.2449463
Mean	429.80717	435.92152
Median Absolute Deviation (MAD)	221	222.5
Skewness	0.1154245	0.032879239
Sum	191694	194421
Variance	65606.205	65997.884
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
278	1	0.2%
187	1	0.2%
1	1	0.2%
331	1	0.2%
833	1	0.2%
103	1	0.2%
448	1	0.2%
220	1	0.2%
784	1	0.2%
641	1	0.2%
Other values (436)	436	97.8%

Value	Count	Frequency (%)
51	1	0.2%
823	1	0.2%
875	1	0.2%
365	1	0.2%
797	1	0.2%
110	1	0.2%
564	1	0.2%
293	1	0.2%
521	1	0.2%
411	1	0.2%
Other values (436)	436	97.8%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
6	1	0.2%
9	1	0.2%
11	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
7	1	0.2%
9	1	0.2%
11	1	0.2%
13	1	0.2%
16	1	0.2%
17	1	0.2%
18	1	0.2%
20	1	0.2%

Value	Count	Frequency (%)
2	1	0.2%
3	1	0.2%
7	1	0.2%
9	1	0.2%
11	1	0.2%
13	1	0.2%
16	1	0.2%
17	1	0.2%
18	1	0.2%
20	1	0.2%

Value	Count	Frequency (%)
1	1	0.2%
2	1	0.2%
6	1	0.2%
9	1	0.2%
11	1	0.2%
12	1	0.2%
13	1	0.2%
14	1	0.2%
15	1	0.2%
16	1	0.2%

Survived
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

0	282
1	164

0	268
1	178

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	2	2
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	0	0
2nd row	0	1
3rd row	1	1
4th row	0	1
5th row	0	1

Common Values

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Value	Count	Frequency (%)
0	268	60.1%
1	178	39.9%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Value	Count	Frequency (%)
0	268	60.1%
1	178	39.9%

Most occurring characters

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Value	Count	Frequency (%)
0	268	60.1%
1	178	39.9%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Value	Count	Frequency (%)
0	268	60.1%
1	178	39.9%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Value	Count	Frequency (%)
0	268	60.1%
1	178	39.9%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
0	282	63.2%
1	164	36.8%

Value	Count	Frequency (%)
0	268	60.1%
1	178	39.9%

Pclass
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

3	243
1	110
2	93

3	245
1	105
2	96

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	446	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	2	3
2nd row	2	3
3rd row	3	3
4th row	3	3
5th row	3	3

Common Values

Value	Count	Frequency (%)
3	243	54.5%
1	110	24.7%
2	93	20.9%

Value	Count	Frequency (%)
3	245	54.9%
1	105	23.5%
2	96	21.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
3	243	54.5%
1	110	24.7%
2	93	20.9%

Value	Count	Frequency (%)
3	245	54.9%
1	105	23.5%
2	96	21.5%

Most occurring characters

Value	Count	Frequency (%)
3	243	54.5%
1	110	24.7%
2	93	20.9%

Value	Count	Frequency (%)
3	245	54.9%
1	105	23.5%
2	96	21.5%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	243	54.5%
1	110	24.7%
2	93	20.9%

Value	Count	Frequency (%)
3	245	54.9%
1	105	23.5%
2	96	21.5%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	243	54.5%
1	110	24.7%
2	93	20.9%

Value	Count	Frequency (%)
3	245	54.9%
1	105	23.5%
2	96	21.5%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	446	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	243	54.5%
1	110	24.7%
2	93	20.9%

Value	Count	Frequency (%)
3	245	54.9%
1	105	23.5%
2	96	21.5%

Name
['Text', 'Text']

	Dataset A	Dataset B
Distinct	446	446
Distinct (%)	100.0%	100.0%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	82	82
Median length	48	49
Mean length	27.029148	26.921525
Min length	13	12

Characters and Unicode

	Dataset A	Dataset B
Total characters	12055	12007
Distinct characters	59	60
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	446	446 ?
Unique (%)	100.0%	100.0%

Sample

	Dataset A	Dataset B
1st row	Parkes, Mr. Francis "Frank"	Panula, Master. Juha Niilo
2nd row	Hold, Mr. Stephen	Madsen, Mr. Fridtjof Arne
3rd row	Moor, Mrs. (Beila)	Stranden, Mr. Juho
4th row	Coleff, Mr. Satio	Peter, Mrs. Catherine (Catherine Rizk)
5th row	Flynn, Mr. James	Mannion, Miss. Margareth

Value	Count	Frequency (%)
mr	265	14.5%
miss	90	4.9%
mrs	57	3.1%
william	33	1.8%
john	25	1.4%
master	23	1.3%
henry	22	1.2%
george	15	0.8%
james	15	0.8%
thomas	12	0.7%
Other values (886)	1265	69.4%

Value	Count	Frequency (%)
mr	257	14.1%
miss	94	5.2%
mrs	68	3.7%
william	34	1.9%
john	24	1.3%
master	18	1.0%
george	13	0.7%
edward	12	0.7%
charles	12	0.7%
henry	11	0.6%
Other values (916)	1276	70.1%

Most occurring characters

Value	Count	Frequency (%)
	1377	11.4%
r	1009	8.4%
e	883	7.3%
a	799	6.6%
i	651	5.4%
n	641	5.3%
s	638	5.3%
M	560	4.6%
l	525	4.4%
o	523	4.3%
Other values (49)	4449	36.9%

Value	Count	Frequency (%)
	1375	11.5%
r	949	7.9%
e	855	7.1%
a	813	6.8%
i	662	5.5%
n	650	5.4%
s	641	5.3%
M	559	4.7%
l	539	4.5%
o	520	4.3%
Other values (50)	4444	37.0%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	12055	100.0%

Value	Count	Frequency (%)
(unknown)	12007	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
	1377	11.4%
r	1009	8.4%
e	883	7.3%
a	799	6.6%
i	651	5.4%
n	641	5.3%
s	638	5.3%
M	560	4.6%
l	525	4.4%
o	523	4.3%
Other values (49)	4449	36.9%

Value	Count	Frequency (%)
	1375	11.5%
r	949	7.9%
e	855	7.1%
a	813	6.8%
i	662	5.5%
n	650	5.4%
s	641	5.3%
M	559	4.7%
l	539	4.5%
o	520	4.3%
Other values (50)	4444	37.0%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	12055	100.0%

Value	Count	Frequency (%)
(unknown)	12007	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
	1377	11.4%
r	1009	8.4%
e	883	7.3%
a	799	6.6%
i	651	5.4%
n	641	5.3%
s	638	5.3%
M	560	4.6%
l	525	4.4%
o	523	4.3%
Other values (49)	4449	36.9%

Value	Count	Frequency (%)
	1375	11.5%
r	949	7.9%
e	855	7.1%
a	813	6.8%
i	662	5.5%
n	650	5.4%
s	641	5.3%
M	559	4.7%
l	539	4.5%
o	520	4.3%
Other values (50)	4444	37.0%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	12055	100.0%

Value	Count	Frequency (%)
(unknown)	12007	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
	1377	11.4%
r	1009	8.4%
e	883	7.3%
a	799	6.6%
i	651	5.4%
n	641	5.3%
s	638	5.3%
M	560	4.6%
l	525	4.4%
o	523	4.3%
Other values (49)	4449	36.9%

Value	Count	Frequency (%)
	1375	11.5%
r	949	7.9%
e	855	7.1%
a	813	6.8%
i	662	5.5%
n	650	5.4%
s	641	5.3%
M	559	4.7%
l	539	4.5%
o	520	4.3%
Other values (50)	4444	37.0%

Sex
Categorical

	Dataset A	Dataset B
Distinct	2	2
Distinct (%)	0.4%	0.4%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

male	299
female	147

male	283
female	163

Length

	Dataset A	Dataset B
Max length	6	6
Median length	4	4
Mean length	4.6591928	4.7309417
Min length	4	4

Characters and Unicode

	Dataset A	Dataset B
Total characters	2078	2110
Distinct characters	5	5
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	male	male
2nd row	male	male
3rd row	female	male
4th row	male	female
5th row	male	female

Common Values

Value	Count	Frequency (%)
male	299	67.0%
female	147	33.0%

Value	Count	Frequency (%)
male	283	63.5%
female	163	36.5%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
male	299	67.0%
female	147	33.0%

Value	Count	Frequency (%)
male	283	63.5%
female	163	36.5%

Most occurring characters

Value	Count	Frequency (%)
e	593	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	147	7.1%

Value	Count	Frequency (%)
e	609	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	163	7.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	2078	100.0%

Value	Count	Frequency (%)
(unknown)	2110	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
e	593	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	147	7.1%

Value	Count	Frequency (%)
e	609	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	163	7.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	2078	100.0%

Value	Count	Frequency (%)
(unknown)	2110	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
e	593	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	147	7.1%

Value	Count	Frequency (%)
e	609	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	163	7.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	2078	100.0%

Value	Count	Frequency (%)
(unknown)	2110	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
e	593	28.5%
m	446	21.5%
a	446	21.5%
l	446	21.5%
f	147	7.1%

Value	Count	Frequency (%)
e	609	28.9%
m	446	21.1%
a	446	21.1%
l	446	21.1%
f	163	7.7%

Age
Real number (ℝ)

	Dataset A	Dataset B
Distinct	73	76
Distinct (%)	20.2%	21.2%
Missing	84	87
Missing (%)	18.8%	19.5%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	29.606133	28.957047

	Dataset A	Dataset B
Minimum	0.42	0.75
Maximum	80	74
Zeros	0	0
Zeros (%)	0.0%	0.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0.42	0.75
5-th percentile	4	4
Q1	21	20.25
median	28	28
Q3	38	36
95-th percentile	55.95	54
Maximum	80	74
Range	79.58	73.25
Interquartile range (IQR)	17	15.75

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	14.524854	14.014288
Coefficient of variation (CV)	0.49060287	0.48396813
Kurtosis	0.15947525	0.18745704
Mean	29.606133	28.957047
Median Absolute Deviation (MAD)	8	8
Skewness	0.38757552	0.38215533
Sum	10717.42	10395.58
Variance	210.97138	196.40027
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
24	20	4.5%
18	15	3.4%
28	14	3.1%
22	12	2.7%
21	12	2.7%
25	12	2.7%
30	12	2.7%
35	11	2.5%
27	10	2.2%
36	10	2.2%
Other values (63)	234	52.5%
(Missing)	84	18.8%

Value	Count	Frequency (%)
24	18	4.0%
28	15	3.4%
22	14	3.1%
18	13	2.9%
21	13	2.9%
30	13	2.9%
29	13	2.9%
25	13	2.9%
19	11	2.5%
26	10	2.2%
Other values (66)	226	50.7%
(Missing)	87	19.5%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.83	1	0.2%
0.92	1	0.2%
1	2	0.4%
2	6	1.3%
3	3	0.7%
4	6	1.3%
5	2	0.4%
7	2	0.4%

Value	Count	Frequency (%)
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	5	1.1%
5	1	0.2%
6	1	0.2%
7	3	0.7%

Value	Count	Frequency (%)
0.75	2	0.4%
0.83	2	0.4%
0.92	1	0.2%
1	3	0.7%
2	6	1.3%
3	1	0.2%
4	5	1.1%
5	1	0.2%
6	1	0.2%
7	3	0.7%

Value	Count	Frequency (%)
0.42	1	0.2%
0.75	1	0.2%
0.83	1	0.2%
0.92	1	0.2%
1	2	0.4%
2	6	1.3%
3	3	0.7%
4	6	1.3%
5	2	0.4%
7	2	0.4%

SibSp
Real number (ℝ)

	Dataset A	Dataset B
Distinct	7	7
Distinct (%)	1.6%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.53811659	0.52690583

	Dataset A	Dataset B
Minimum	0	0
Maximum	8	8
Zeros	306	293
Zeros (%)	68.6%	65.7%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	1	1
95-th percentile	3	2
Maximum	8	8
Range	8	8
Interquartile range (IQR)	1	1

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	1.108549	0.97747339
Coefficient of variation (CV)	2.0600536	1.8551197
Kurtosis	15.487631	12.147253
Mean	0.53811659	0.52690583
Median Absolute Deviation (MAD)	0	0
Skewness	3.4129477	2.9624521
Sum	240	235
Variance	1.2288809	0.95545422
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=7)

Value	Count	Frequency (%)
0	306	68.6%
1	95	21.3%
2	20	4.5%
3	10	2.2%
4	9	2.0%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	293	65.7%
1	113	25.3%
2	18	4.0%
4	9	2.0%
3	9	2.0%
5	3	0.7%
8	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	306	68.6%
1	95	21.3%
2	20	4.5%
3	10	2.2%
4	9	2.0%
5	3	0.7%
8	3	0.7%

Value	Count	Frequency (%)
0	293	65.7%
1	113	25.3%
2	18	4.0%
3	9	2.0%
4	9	2.0%
5	3	0.7%
8	1	0.2%

Value	Count	Frequency (%)
0	293	65.7%
1	113	25.3%
2	18	4.0%
3	9	2.0%
4	9	2.0%
5	3	0.7%
8	1	0.2%

Value	Count	Frequency (%)
0	306	68.6%
1	95	21.3%
2	20	4.5%
3	10	2.2%
4	9	2.0%
5	3	0.7%
8	3	0.7%

Parch
Real number (ℝ)

	Dataset A	Dataset B
Distinct	6	7
Distinct (%)	1.3%	1.6%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	0.39910314	0.39237668

	Dataset A	Dataset B
Minimum	0	0
Maximum	5	6
Zeros	337	339
Zeros (%)	75.6%	76.0%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	0	0
Q1	0	0
median	0	0
Q3	0	0
95-th percentile	2	2
Maximum	5	6
Range	5	6
Interquartile range (IQR)	0	0

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	0.83593403	0.82425871
Coefficient of variation (CV)	2.0945313	2.1006822
Kurtosis	8.8810356	9.9949191
Mean	0.39910314	0.39237668
Median Absolute Deviation (MAD)	0	0
Skewness	2.6870051	2.7416945
Sum	178	175
Variance	0.69878571	0.67940243
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=6)

Value	Count	Frequency (%)
0	337	75.6%
1	59	13.2%
2	41	9.2%
5	4	0.9%
3	3	0.7%
4	2	0.4%

Value	Count	Frequency (%)
0	339	76.0%
1	55	12.3%
2	45	10.1%
3	2	0.4%
5	2	0.4%
4	2	0.4%
6	1	0.2%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	337	75.6%
1	59	13.2%
2	41	9.2%
3	3	0.7%
4	2	0.4%
5	4	0.9%

Value	Count	Frequency (%)
0	339	76.0%
1	55	12.3%
2	45	10.1%
3	2	0.4%
4	2	0.4%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	339	76.0%
1	55	12.3%
2	45	10.1%
3	2	0.4%
4	2	0.4%
5	2	0.4%
6	1	0.2%

Value	Count	Frequency (%)
0	337	75.6%
1	59	13.2%
2	41	9.2%
3	3	0.7%
4	2	0.4%
5	4	0.9%

Ticket
['Text', 'Text']

	Dataset A	Dataset B
Distinct	379	384
Distinct (%)	85.0%	86.1%
Missing	0	0
Missing (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	18	18
Median length	17	17
Mean length	6.7443946	6.8475336
Min length	4	3

Characters and Unicode

	Dataset A	Dataset B
Total characters	3008	3054
Distinct characters	31	32
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	331	339 ?
Unique (%)	74.2%	76.0%

Sample

	Dataset A	Dataset B
1st row	239853	3101295
2nd row	26707	C 17369
3rd row	392096	STON/O 2. 3101288
4th row	349209	2668
5th row	364851	36866

Value	Count	Frequency (%)
pc	31	5.5%
c.a	14	2.5%
a/5	10	1.8%
ston/o	7	1.2%
2	7	1.2%
ca	7	1.2%
w./c	6	1.1%
347082	5	0.9%
soton/oq	5	0.9%
soton/o.q	4	0.7%
Other values (396)	472	83.1%

Value	Count	Frequency (%)
pc	27	4.7%
c.a	14	2.4%
a/5	10	1.7%
ston/o	8	1.4%
2	8	1.4%
w./c	6	1.0%
soton/o.q	6	1.0%
ca	6	1.0%
sc/paris	5	0.9%
soton/oq	4	0.7%
Other values (403)	480	83.6%

Most occurring characters

Value	Count	Frequency (%)
3	370	12.3%
1	338	11.2%
2	302	10.0%
7	254	8.4%
4	222	7.4%
6	218	7.2%
0	199	6.6%
5	186	6.2%
8	154	5.1%
9	154	5.1%
Other values (21)	611	20.3%

Value	Count	Frequency (%)
3	364	11.9%
1	347	11.4%
2	292	9.6%
4	236	7.7%
7	234	7.7%
0	219	7.2%
6	208	6.8%
5	188	6.2%
9	167	5.5%
8	147	4.8%
Other values (22)	652	21.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	3008	100.0%

Value	Count	Frequency (%)
(unknown)	3054	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
3	370	12.3%
1	338	11.2%
2	302	10.0%
7	254	8.4%
4	222	7.4%
6	218	7.2%
0	199	6.6%
5	186	6.2%
8	154	5.1%
9	154	5.1%
Other values (21)	611	20.3%

Value	Count	Frequency (%)
3	364	11.9%
1	347	11.4%
2	292	9.6%
4	236	7.7%
7	234	7.7%
0	219	7.2%
6	208	6.8%
5	188	6.2%
9	167	5.5%
8	147	4.8%
Other values (22)	652	21.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	3008	100.0%

Value	Count	Frequency (%)
(unknown)	3054	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
3	370	12.3%
1	338	11.2%
2	302	10.0%
7	254	8.4%
4	222	7.4%
6	218	7.2%
0	199	6.6%
5	186	6.2%
8	154	5.1%
9	154	5.1%
Other values (21)	611	20.3%

Value	Count	Frequency (%)
3	364	11.9%
1	347	11.4%
2	292	9.6%
4	236	7.7%
7	234	7.7%
0	219	7.2%
6	208	6.8%
5	188	6.2%
9	167	5.5%
8	147	4.8%
Other values (22)	652	21.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	3008	100.0%

Value	Count	Frequency (%)
(unknown)	3054	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
3	370	12.3%
1	338	11.2%
2	302	10.0%
7	254	8.4%
4	222	7.4%
6	218	7.2%
0	199	6.6%
5	186	6.2%
8	154	5.1%
9	154	5.1%
Other values (21)	611	20.3%

Value	Count	Frequency (%)
3	364	11.9%
1	347	11.4%
2	292	9.6%
4	236	7.7%
7	234	7.7%
0	219	7.2%
6	208	6.8%
5	188	6.2%
9	167	5.5%
8	147	4.8%
Other values (22)	652	21.3%

Fare
Real number (ℝ)

	Dataset A	Dataset B
Distinct	185	176
Distinct (%)	41.5%	39.5%
Missing	0	0
Missing (%)	0.0%	0.0%
Infinite	0	0
Infinite (%)	0.0%	0.0%
Mean	32.388554	32.95637

	Dataset A	Dataset B
Minimum	0	0
Maximum	512.3292	512.3292
Zeros	9	7
Zeros (%)	2.0%	1.6%
Negative	0	0
Negative (%)	0.0%	0.0%
Memory size	7.0 KiB	7.0 KiB

Quantile statistics

	Dataset A	Dataset B
Minimum	0	0
5-th percentile	7.125	7.162525
Q1	7.8958	7.925
median	13.93125	14.45625
Q3	31.275	30.375
95-th percentile	118.31875	120
Maximum	512.3292	512.3292
Range	512.3292	512.3292
Interquartile range (IQR)	23.3792	22.45

Descriptive statistics

	Dataset A	Dataset B
Standard deviation	48.137637	50.150937
Coefficient of variation (CV)	1.4862546	1.5217373
Kurtosis	27.718145	24.872053
Mean	32.388554	32.95637
Median Absolute Deviation (MAD)	6.70205	6.81665
Skewness	4.2873395	4.1680823
Sum	14445.295	14698.541
Variance	2317.2321	2515.1165
Monotonicity	Not monotonic	Not monotonic

Histogram with fixed size bins (bins=50)

Value	Count	Frequency (%)
8.05	22	4.9%
13	19	4.3%
7.8958	19	4.3%
10.5	16	3.6%
7.75	10	2.2%
26	10	2.2%
0	9	2.0%
26.55	8	1.8%
7.2292	8	1.8%
7.925	8	1.8%
Other values (175)	317	71.1%

Value	Count	Frequency (%)
7.8958	24	5.4%
26	22	4.9%
8.05	20	4.5%
13	18	4.0%
7.75	15	3.4%
10.5	12	2.7%
7.925	12	2.7%
7.775	10	2.2%
8.6625	9	2.0%
0	7	1.6%
Other values (166)	297	66.6%

Minimum 10 values
Maximum 10 values

Value	Count	Frequency (%)
0	9	2.0%
6.45	1	0.2%
6.4958	2	0.4%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	2	0.4%
7.05	3	0.7%
7.0542	1	0.2%
7.125	3	0.7%

Value	Count	Frequency (%)
0	7	1.6%
5	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.95	1	0.2%
7.0458	1	0.2%
7.05	4	0.9%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	7	1.6%
5	1	0.2%
6.4375	1	0.2%
6.45	1	0.2%
6.4958	1	0.2%
6.75	1	0.2%
6.95	1	0.2%
7.0458	1	0.2%
7.05	4	0.9%
7.0542	2	0.4%

Value	Count	Frequency (%)
0	9	2.0%
6.45	1	0.2%
6.4958	2	0.4%
6.75	1	0.2%
6.8583	1	0.2%
6.95	1	0.2%
6.975	2	0.4%
7.05	3	0.7%
7.0542	1	0.2%
7.125	3	0.7%

Cabin
['Text', 'Text']

	Dataset A	Dataset B
Distinct	88	88
Distinct (%)	85.4%	86.3%
Missing	343	344
Missing (%)	76.9%	77.1%
Memory size	7.0 KiB	7.0 KiB

Length

	Dataset A	Dataset B
Max length	15	15
Median length	3	3
Mean length	3.5436893	3.8529412
Min length	2	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	365	393
Distinct characters	18	19
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	76	77 ?
Unique (%)	73.8%	75.5%

Sample

	Dataset A	Dataset B
1st row	E121	E10
2nd row	C65	D48
3rd row	A10	E8
4th row	B35	E63
5th row	C22 C26	E40

Value	Count	Frequency (%)
c22	3	2.5%
f2	3	2.5%
f33	3	2.5%
c26	3	2.5%
c92	2	1.7%
e101	2	1.7%
b5	2	1.7%
c25	2	1.7%
c23	2	1.7%
c27	2	1.7%
Other values (89)	94	79.7%

Value	Count	Frequency (%)
c22	3	2.4%
b96	3	2.4%
b98	3	2.4%
c23	3	2.4%
c25	3	2.4%
c27	3	2.4%
c26	3	2.4%
c2	2	1.6%
e8	2	1.6%
g6	2	1.6%
Other values (91)	99	78.6%

Most occurring characters

Value	Count	Frequency (%)
C	41	11.2%
2	38	10.4%
1	34	9.3%
3	32	8.8%
B	26	7.1%
6	24	6.6%
0	21	5.8%
5	20	5.5%
8	19	5.2%
4	16	4.4%
Other values (8)	94	25.8%

Value	Count	Frequency (%)
C	40	10.2%
2	40	10.2%
1	36	9.2%
B	34	8.7%
6	32	8.1%
5	30	7.6%
	24	6.1%
7	21	5.3%
3	20	5.1%
D	19	4.8%
Other values (9)	97	24.7%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	365	100.0%

Value	Count	Frequency (%)
(unknown)	393	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
C	41	11.2%
2	38	10.4%
1	34	9.3%
3	32	8.8%
B	26	7.1%
6	24	6.6%
0	21	5.8%
5	20	5.5%
8	19	5.2%
4	16	4.4%
Other values (8)	94	25.8%

Value	Count	Frequency (%)
C	40	10.2%
2	40	10.2%
1	36	9.2%
B	34	8.7%
6	32	8.1%
5	30	7.6%
	24	6.1%
7	21	5.3%
3	20	5.1%
D	19	4.8%
Other values (9)	97	24.7%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	365	100.0%

Value	Count	Frequency (%)
(unknown)	393	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
C	41	11.2%
2	38	10.4%
1	34	9.3%
3	32	8.8%
B	26	7.1%
6	24	6.6%
0	21	5.8%
5	20	5.5%
8	19	5.2%
4	16	4.4%
Other values (8)	94	25.8%

Value	Count	Frequency (%)
C	40	10.2%
2	40	10.2%
1	36	9.2%
B	34	8.7%
6	32	8.1%
5	30	7.6%
	24	6.1%
7	21	5.3%
3	20	5.1%
D	19	4.8%
Other values (9)	97	24.7%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	365	100.0%

Value	Count	Frequency (%)
(unknown)	393	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
C	41	11.2%
2	38	10.4%
1	34	9.3%
3	32	8.8%
B	26	7.1%
6	24	6.6%
0	21	5.8%
5	20	5.5%
8	19	5.2%
4	16	4.4%
Other values (8)	94	25.8%

Value	Count	Frequency (%)
C	40	10.2%
2	40	10.2%
1	36	9.2%
B	34	8.7%
6	32	8.1%
5	30	7.6%
	24	6.1%
7	21	5.3%
3	20	5.1%
D	19	4.8%
Other values (9)	97	24.7%

Embarked
Categorical

	Dataset A	Dataset B
Distinct	3	3
Distinct (%)	0.7%	0.7%
Missing	1	0
Missing (%)	0.2%	0.0%
Memory size	7.0 KiB	7.0 KiB

S	326
C	82
Q	37

S	333
C	76
Q	37

Length

	Dataset A	Dataset B
Max length	1	1
Median length	1	1
Mean length	1	1
Min length	1	1

Characters and Unicode

	Dataset A	Dataset B
Total characters	445	446
Distinct characters	3	3
Distinct categories	1	1 ?
Distinct scripts	1	1 ?
Distinct blocks	1	1 ?

The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

	Dataset A	Dataset B
Unique	0	0 ?
Unique (%)	0.0%	0.0%

Sample

	Dataset A	Dataset B
1st row	S	S
2nd row	S	S
3rd row	S	S
4th row	S	C
5th row	Q	Q

Common Values

Value	Count	Frequency (%)
S	326	73.1%
C	82	18.4%
Q	37	8.3%
(Missing)	1	0.2%

Value	Count	Frequency (%)
S	333	74.7%
C	76	17.0%
Q	37	8.3%

Length

Histogram of lengths of the category

Common Values (Plot)

Dataset A

Dataset B

Value	Count	Frequency (%)
s	326	73.3%
c	82	18.4%
q	37	8.3%

Value	Count	Frequency (%)
s	333	74.7%
c	76	17.0%
q	37	8.3%

Most occurring characters

Value	Count	Frequency (%)
S	326	73.3%
C	82	18.4%
Q	37	8.3%

Value	Count	Frequency (%)
S	333	74.7%
C	76	17.0%
Q	37	8.3%

Most occurring categories

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per category

(unknown)

Value	Count	Frequency (%)
S	326	73.3%
C	82	18.4%
Q	37	8.3%

Value	Count	Frequency (%)
S	333	74.7%
C	76	17.0%
Q	37	8.3%

Most occurring scripts

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per script

(unknown)

Value	Count	Frequency (%)
S	326	73.3%
C	82	18.4%
Q	37	8.3%

Value	Count	Frequency (%)
S	333	74.7%
C	76	17.0%
Q	37	8.3%

Most occurring blocks

Value	Count	Frequency (%)
(unknown)	445	100.0%

Value	Count	Frequency (%)
(unknown)	446	100.0%

Most frequent character per block

(unknown)

Value	Count	Frequency (%)
S	326	73.3%
C	82	18.4%
Q	37	8.3%

Value	Count	Frequency (%)
S	333	74.7%
C	76	17.0%
Q	37	8.3%

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset B

Dataset A

Dataset A

A simple visualization of nullity by column.

Dataset B

A simple visualization of nullity by column.

Dataset A

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Dataset B

Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

First rows
Last rows

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
277	278	0	2	Parkes, Mr. Francis "Frank"	male	NaN	0	0	239853	0.0000	NaN	S
236	237	0	2	Hold, Mr. Stephen	male	44.0	1	0	26707	26.0000	NaN	S
823	824	1	3	Moor, Mrs. (Beila)	female	27.0	0	1	392096	12.4750	E121	S
514	515	0	3	Coleff, Mr. Satio	male	24.0	0	0	349209	7.4958	NaN	S
428	429	0	3	Flynn, Mr. James	male	NaN	0	0	364851	7.7500	NaN	Q
774	775	1	2	Hocking, Mrs. Elizabeth (Eliza Needs)	female	54.0	1	3	29105	23.0000	NaN	S
307	308	1	1	Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo)	female	17.0	1	0	PC 17758	108.9000	C65	C
624	625	0	3	Bowen, Mr. David John "Dai"	male	21.0	0	0	54636	16.1000	NaN	S
583	584	0	1	Ross, Mr. John Hugo	male	36.0	0	0	13049	40.1250	A10	C
355	356	0	3	Vanden Steen, Mr. Leo Peter	male	28.0	0	0	345783	9.5000	NaN	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
50	51	0	3	Panula, Master. Juha Niilo	male	7.0	4	1	3101295	39.6875	NaN	S
127	128	1	3	Madsen, Mr. Fridtjof Arne	male	24.0	0	0	C 17369	7.1417	NaN	S
744	745	1	3	Stranden, Mr. Juho	male	31.0	0	0	STON/O 2. 3101288	7.9250	NaN	S
533	534	1	3	Peter, Mrs. Catherine (Catherine Rizk)	female	NaN	0	2	2668	22.3583	NaN	C
727	728	1	3	Mannion, Miss. Margareth	female	NaN	0	0	36866	7.7375	NaN	Q
274	275	1	3	Healy, Miss. Hanora "Nora"	female	NaN	0	0	370375	7.7500	NaN	Q
694	695	0	1	Weir, Col. John	male	60.0	0	0	113800	26.5500	NaN	S
471	472	0	3	Cacic, Mr. Luka	male	38.0	0	0	315089	8.6625	NaN	S
429	430	1	3	Pickard, Mr. Berk (Berk Trembisky)	male	32.0	0	0	SOTON/O.Q. 392078	8.0500	E10	S
729	730	0	3	Ilmakangas, Miss. Pieta Sofia	female	25.0	1	0	STON/O2. 3101271	7.9250	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
83	84	0	1	Carrau, Mr. Francisco M	male	28.0	0	113059	47.1000	NaN	S
847	848	0	3	Markoff, Mr. Marin	male	35.0	0	349213	7.8958	NaN	C
191	192	0	2	Carbines, Mr. William	male	19.0	0	28424	13.0000	NaN	S
743	744	0	3	McNamee, Mr. Neal	male	24.0	1	376566	16.1000	NaN	S
12	13	0	3	Saundercock, Mr. William Henry	male	20.0	0	A/5. 2151	8.0500	NaN	S
92	93	0	1	Chaffee, Mr. Herbert Fuller	male	46.0	1	W.E.P. 5734	61.1750	E31	S
213	214	0	2	Givard, Mr. Hans Kristensen	male	30.0	0	250646	13.0000	NaN	S
304	305	0	3	Williams, Mr. Howard Hugh "Harry"	male	NaN	0	A/5 2466	8.0500	NaN	S
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	113572	80.0000	B28	NaN
185	186	0	1	Rood, Mr. Hugh Roscoe	male	NaN	0	113767	50.0000	A32	S

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
232	233	0	2	Sjostedt, Mr. Ernst Adolf	male	59.0	0	0	237442	13.5000	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Thayer)	female	38.0	1	0	PC 17599	71.2833	C85	C
616	617	0	3	Danbom, Mr. Ernst Gilbert	male	34.0	1	1	347080	14.4000	NaN	S
480	481	0	3	Goodwin, Master. Harold Victor	male	9.0	5	2	CA 2144	46.9000	NaN	S
234	235	0	2	Leyson, Mr. Robert William Norman	male	24.0	0	0	C.A. 29566	10.5000	NaN	S
93	94	0	3	Dean, Mr. Bertram Frank	male	26.0	1	2	C.A. 2315	20.5750	NaN	S
830	831	1	3	Yasbeck, Mrs. Antoni (Selini Alexander)	female	15.0	1	0	2659	14.4542	NaN	C
716	717	1	1	Endres, Miss. Caroline Louise	female	38.0	0	0	PC 17757	227.5250	C45	C
585	586	1	1	Taussig, Miss. Ruth	female	18.0	0	2	110413	79.6500	E68	S
771	772	0	3	Jensen, Mr. Niels Peder	male	48.0	0	0	350047	7.8542	NaN	S

Dataset A

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Dataset B

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	# duplicates
Dataset does not contain duplicate rows.

Overview

Variables

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)

Dataset A

Dataset B

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Most occurring characters

Most occurring categories

Most frequent character per category

(unknown)

Most occurring scripts

Most frequent character per script

(unknown)

Most occurring blocks

Most frequent character per block

(unknown)

Common Values

Length

Common Values (Plot)